Local statistics allow quantification of cell-to-cell variability from high-throughput microscope images.

Area of Science:

• Systems biology
• Cell biology
• Microscopy image analysis

Background:

• Quantifying protein expression variability is crucial in systems biology.
• Cell-to-cell variability in subcellular protein localization remains underquantified.

Purpose of the Study:

• To develop and apply a quantitative measure for cell-to-cell variability in protein localization patterns.
• To systematically assess this variability in a large-scale microscopy dataset.

Main Methods:

• Definition of a local measure for quantifying cell-to-cell variability.
• Application of this measure to high-throughput microscopy images.
• Systematic estimation of variability in the yeast GFP collection.

Main Results:

• A novel local measure effectively quantifies cell-to-cell variability across diverse protein localizations.
• Systematic analysis of the yeast GFP collection identified proteins exhibiting significant localization variability.
• Demonstrated comparability of variability measures for proteins with different subcellular distributions.

Conclusions:

• Automated image analysis provides a robust framework for quantifying cell-to-cell variability in protein localization.
• This methodology enables systematic investigation of protein expression heterogeneity at the subcellular level.